{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四回：文字图例尽眉目"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、Figure和Axes上的文本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib具有广泛的文本支持，包括对数学表达式的支持、对栅格和矢量输出的TrueType支持、具有任意旋转的换行分隔文本以及Unicode支持。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的命令是介绍了通过pyplot API和objected-oriented API分别创建文本的方式。\n",
    "\n",
    "| [`pyplot`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot) API | OO API                                                       | description                                                  |\n",
    "| :----------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |\n",
    "| [`text`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text) | [`text`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text) | 在 [`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的任意位置添加text。 |\n",
    "| [`title`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title) | [`set_title`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title) | 在 [`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)添加title |\n",
    "| [`figtext`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figtext.html#matplotlib.pyplot.figtext) | [`text`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.text) | 在[`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure)的任意位置添加text. |\n",
    "| [`suptitle`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.suptitle.html#matplotlib.pyplot.suptitle) | [`suptitle`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.suptitle) | 在 [`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure)添加title |\n",
    "| [`xlabel`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel) | [`set_xlabel`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel) | 在[`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的x-axis添加label |\n",
    "| [`ylabel`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel) | [`set_ylabel`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel) | 在[`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的y-axis添加label |\n",
    "| [`annotate`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.annotate.html#matplotlib.pyplot.annotate) | [`annotate`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.annotate.html#matplotlib.axes.Axes.annotate) | 向[`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的任意位置添加带有可选箭头的标注. |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.text\n",
    "pyplot API：matplotlib.pyplot.text(x, y, s, fontdict=None, \\*\\*kwargs)  \n",
    "OO API:Axes.text(self, x, y, s, fontdict=None, \\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数：  \n",
    "s:此参数是要添加的文本。  \n",
    "xy:此参数是放置文本的点(x，y)。  \n",
    "fontdict:此参数是一个可选参数，并且是一个覆盖默认文本属性的字典。如果fontdict为None，则由rcParams确定默认值。  \n",
    "**返回值**：此方法返回作为创建的文本实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fontdict主要参数具体介绍，更多参数请参考[官网说明](https://matplotlib.org/api/text_api.html?highlight=text#matplotlib.text.Text)：\n",
    "\n",
    "\n",
    "| Property                                                     | Description                                                  |\n",
    "| ------------------------------------------------------------ | :----------------------------------------------------------- |\n",
    "| [`alpha`](https://matplotlib.org/api/_as_gen/matplotlib.artist.Artist.set_alpha.html#matplotlib.artist.Artist.set_alpha) |float or None   该参数指透明度，越接近0越透明，越接近1越不透明   |                           \n",
    "| [`backgroundcolor`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_backgroundcolor) | color  [该参数指文本的背景颜色，具体matplotlib支持颜色如下](https://www.cnblogs.com/charliedaifu/p/9957822.html)                                                    |\n",
    "| [`bbox`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_bbox) | dict with properties for [`patches.FancyBboxPatch`](https://matplotlib.org/api/_as_gen/matplotlib.patches.FancyBboxPatch.html#matplotlib.patches.FancyBboxPatch) 这个是用来设置text周围的box外框 |\n",
    "| [`color`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_color) or c | color 指的是字体的颜色                                                       |\n",
    "| [`fontfamily`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontfamily) or family | {FONTNAME, 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace'} 该参数指的是字体的类型|\n",
    "| [`fontproperties`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontproperties) or font or font_properties | [`font_manager.FontProperties`](https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties) or [`str`](https://docs.python.org/3/library/stdtypes.html#str) or [`pathlib.Path`](https://docs.python.org/3/library/pathlib.html#pathlib.Path) |\n",
    "| [`fontsize`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontsize) or size | float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'} 该参数指字体大小|\n",
    "| [`fontstretch`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontstretch) or stretch | {a numeric value in range 0-1000, 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded', 'ultra-expanded'} 该参数是指从字体中选择正常、压缩或扩展的字体 |\n",
    "| [`fontstyle`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontstyle) or style | {'normal', 'italic', 'oblique'} 该参数是指字体的样式是否倾斜等                               |\n",
    "| [`fontweight`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontweight) or weight | {a numeric value in range 0-1000, 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black'} |         \n",
    "| [`horizontalalignment`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_horizontalalignment) or ha | {'center', 'right', 'left'}  该参数是指选择文本左对齐右对齐还是居中对齐                                |\n",
    "| [`label`](https://matplotlib.org/api/_as_gen/matplotlib.artist.Artist.set_label.html#matplotlib.artist.Artist.set_label) | object                                                       |\n",
    "| [`linespacing`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_linespacing) | float (multiple of font size)                                |\n",
    "| [`position`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_position) | (float, float)                                               |\n",
    "| [`rotation`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_rotation) | float or {'vertical', 'horizontal'} 该参数是指text逆时针旋转的角度，“horizontal”等于0，“vertical”等于90。我们可以根据自己设定来选择合适角度                       |\n",
    "| [`verticalalignment`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_verticalalignment) or va | {'center', 'top', 'bottom', 'baseline', 'center_baseline'}   |\n",
    "                                                    |\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fontdict学习的案例\n",
    "#学习的过程中请尝试更换不同的fontdict字典的内容，以便于更好的掌握\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#---------设置字体样式，分别是字体，颜色，宽度，大小\n",
    "font1 = {'family': 'Times New Roman',\n",
    "        'color':  'purple',\n",
    "        'weight': 'normal',\n",
    "        'size': 16,\n",
    "        }\n",
    "font2 = {'family': 'Times New Roman',\n",
    "        'color':  'red',\n",
    "        'weight': 'normal',\n",
    "        'size': 16,\n",
    "        }\n",
    "font3 = {'family': 'serif',\n",
    "        'color':  'blue',\n",
    "        'weight': 'bold',\n",
    "        'size': 14,\n",
    "        }\n",
    "font4 = {'family': 'Calibri',\n",
    "        'color':  'navy',\n",
    "        'weight': 'normal',\n",
    "        'size': 17,\n",
    "        }\n",
    "#-----------四种不同字体显示风格-----\n",
    " \n",
    "#-------建立函数----------\n",
    "x = np.linspace(0.0, 5.0, 100)\n",
    "y = np.cos(2*np.pi*x) * np.exp(-x/3)\n",
    "#-------绘制图像，添加标注----------\n",
    "plt.plot(x, y, '--')\n",
    "plt.title('Damped exponential decay', fontdict=font1)\n",
    "#------添加文本在指定的坐标处------------\n",
    "plt.text(2, 0.65, r'$\\cos(2 \\pi x) \\exp(-x/3)$', fontdict=font2)\n",
    "#---------设置坐标标签\n",
    "plt.xlabel('Y=time (s)', fontdict=font3)\n",
    "plt.ylabel('X=voltage(mv)', fontdict=font4)\n",
    " \n",
    "# 调整图像边距\n",
    "plt.subplots_adjust(left=0.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.title和set_title\n",
    "pyplot API：matplotlib.pyplot.title(label, fontdict=None, loc=None, pad=None, \\*, y=None, \\*\\*kwargs)  \n",
    "OO API:Axes.set_title(self, label, fontdict=None, loc=None, pad=None, \\*, y=None, \\*\\*kwargs)  \n",
    "该命令是用来设置axes的标题。  \n",
    "**参数**：此方法接受以下描述的参数:  \n",
    "label：str，此参数是要添加的文本  \n",
    "fontdict：dict，此参数是控制title文本的外观，默认fontdict如下：\n",
    "```python\n",
    "{'fontsize': rcParams['axes.titlesize'],\n",
    " 'fontweight': rcParams['axes.titleweight'],\n",
    " 'color': rcParams['axes.titlecolor'],\n",
    " 'verticalalignment': 'baseline',\n",
    " 'horizontalalignment': loc}\n",
    " ```\n",
    "loc:str，{'center', 'left', 'right'}默认为center  \n",
    "pad:float,该参数是指标题偏离图表顶部的距离，默认为6。  \n",
    "y:float，该参数是title所在axes垂向的位置。默认值为1，即title位于axes的顶部。  \n",
    "kwargs：该参数是指可以设置的一些奇特文本的属性。  \n",
    "**返回值**：此方法返回作为创建的title实例的文本。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#该例子展现了如何在title中使用fontdict参数。\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#---------设置字体样式，分别是字体，透明度，颜色，宽度，大小\n",
    "font1 = {'family': 'SimSun',#华文楷体\n",
    "         'alpha':0.7,#透明度\n",
    "        'color':  'purple',\n",
    "        'weight': 'normal',\n",
    "        'size': 16,\n",
    "        }\n",
    "x = np.linspace(0.0, 5.0, 100)\n",
    "y = np.cos(2*np.pi*x) * np.exp(-x/3)\n",
    "#-------绘制图像，添加标注----------\n",
    "plt.plot(x, y, '--')\n",
    "plt.title('震荡曲线', fontdict=font1,pad=13,loc='right')\n",
    "# 调整图像边距\n",
    "plt.subplots_adjust(left=0.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.figtext和text\n",
    "pyplot API：matplotlib.pyplot.figtext(x, y, s, fontdict=None, \\*\\*kwargs)  \n",
    "OO API:text(self, x, y, s, fontdict=None,\\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数:    \n",
    "x,y：float，此参数是指在figure中放置文本的位置。一般取值是在\\[0,1\\]范围内。使用transform关键字可以更改坐标系。  \n",
    "s:str,此参数是指文本  \n",
    "fontdict:dict,此参数是一个可选参数，并且是一个覆盖默认文本属性的字典。如果fontdict为None，则由rcParams确定默认值。  \n",
    "**返回值**：此方法返回作为创建的文本实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.suptitle\n",
    "pyplot API：matplotlib.pyplot.suptitle(t, \\*\\*kwargs)  \n",
    "OO API:suptitle(self, t, \\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数:      \n",
    "t: str,标题的文本  \n",
    "x：float,默认值是0.5.该参数是指文本在figure坐标系下的x坐标  \n",
    "y：float,默认值是0.95.该参数是指文本在figure坐标系下的y坐标  \n",
    "horizontalalignment, ha:该参数是指选择文本水平对齐方式，有三种选择{'center', 'left', right'}，默认值是 'center'  \n",
    "verticalalignment, va：该参数是指选择文本垂直对齐方式，有四种选择{'top', 'center', 'bottom', 'baseline'}，默认值是 'top'  \n",
    "fontsize, size：该参数是指文本的大小，默认值是依据rcParams的设置：rcParams[\"figure.titlesize\"] (default: 'large')  \n",
    "fontweight, weight：该参数是用来设置字重。默认值是依据rcParams的设置：rcParams[\"figure.titleweight\"] (default: 'normal')  \n",
    "[fontproperties](https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties):None or dict,该参数是可选参数，如果该参数被指定，字体的大小将从该参数的默认值中提取。  \n",
    "**返回值**：此方法返回作为创建的title实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.xlabel和ylabel\n",
    "\n",
    "pyplot API：matplotlib.pyplot.xlabel(xlabel, fontdict=None, labelpad=None, \\*, loc=None, \\*\\*kwargs)  \n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;matplotlib.pyplot.ylabel(ylabel, fontdict=None, labelpad=None,\\*, loc=None, \\*\\*kwargs)  \n",
    "OO API: &emsp;Axes.set_xlabel(self, xlabel, fontdict=None, labelpad=None, \\*, loc=None, \\*\\*kwargs)  \n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;Axes.set_ylabel(self, ylabel, fontdict=None, labelpad=None,\\*, loc=None, \\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数:        \n",
    "xlabel或者ylabel：label的文本  \n",
    "labelpad:设置label距离轴(axis)的距离  \n",
    "loc:{'left', 'center', 'right'},默认为center  \n",
    "\\*\\*kwargs:[文本](https://matplotlib.org/api/text_api.html#matplotlib.text.Text)属性  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#文本属性的输入一种是通过**kwargs属性这种方式，一种是通过操作 matplotlib.font_manager.FontProperties 方法\n",
    "#该链接是FontProperties方法的介绍 https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x1 = np.linspace(0.0, 5.0, 100)\n",
    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n",
    "\n",
    "font = FontProperties()\n",
    "font.set_family('serif')\n",
    "font.set_name('Times New Roman')\n",
    "font.set_style('italic')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "fig.subplots_adjust(bottom=0.15, left=0.2)\n",
    "ax.plot(x1, y1)\n",
    "ax.set_xlabel('time [s]', fontsize='large', fontweight='bold')\n",
    "ax.set_ylabel('Damped oscillation [V]', fontproperties=font)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.annotate\n",
    "pyplot API：matplotlib.pyplot.annotate(text, xy, \\*args,\\*\\*kwargs)  \n",
    "OO API:Axes.annotate(self, text, xy, \\*args,\\*\\*kwargs)  \n",
    "\n",
    "**参数**：此方法接受以下描述的参数:          \n",
    "text:str，该参数是指注释文本的内容  \n",
    "xy：该参数接受二维元组(float, float)，是指要注释的点。其二维元组所在的坐标系由xycoords参数决定  \n",
    "xytext：注释文本的坐标点，也是二维元组，默认与xy相同  \n",
    "xycoords:该参数接受   被注释点的坐标系属性，允许的输入值如下:\n",
    "\n",
    "| 属性值            | 含义                                                         |\n",
    "| :---------------- | :----------------------------------------------------------- |\n",
    "| 'figure points'   | 以绘图区左下角为参考，单位是点数                             |\n",
    "| 'figure pixels'   | 以绘图区左下角为参考，单位是像素数                           |\n",
    "| 'figure fraction' | 以绘图区左下角为参考，单位是百分比                           |\n",
    "| 'axes points'     | 以子绘图区左下角为参考，单位是点数（一个figure可以有多个axes，默认为1个） |\n",
    "| 'axes pixels'     | 以子绘图区左下角为参考，单位是像素数                         |\n",
    "| 'axes fraction'   | 以子绘图区左下角为参考，单位是百分比                         |\n",
    "| 'data'            | 以被注释的坐标点xy为参考 (默认值)                            |\n",
    "| 'polar'           | *不使用本地数据坐标系，使用极坐标系*                         |\n",
    "\n",
    "textcoords ：注释文本的坐标系属性，默认与xycoords属性值相同，也可设为不同的值。除了允许输入xycoords的属性值，还允许输入以下两种：\n",
    "\n",
    "| 属性值          | 含义                                   |\n",
    "| :-------------- | :------------------------------------- |\n",
    "| 'offset points' | 相对于被注释点xy的偏移量（单位是点）   |\n",
    "| 'offset pixels' | 相对于被注释点xy的偏移量（单位是像素） |\n",
    "\n",
    "arrowprops：箭头的样式，dict（字典）型数据，如果该属性非空，则会在注释文本和被注释点之间画一个箭头。如果不设置`'arrowstyle'` 关键字，则允许包含以下关键字：\n",
    "\n",
    "| 关键字     | 说明                                                |\n",
    "| :--------- | :-------------------------------------------------- |\n",
    "| width      | 箭头的宽度（单位是点）                              |\n",
    "| headwidth  | 箭头头部的宽度（点）                                |\n",
    "| headlength | 箭头头部的长度（点）                                |\n",
    "| shrink     | 箭头两端收缩的百分比（占总长）                      |\n",
    "| ?          | 任何 [matplotlib.patches.FancyArrowPatch](https://matplotlib.org/api/_as_gen/matplotlib.patches.FancyArrowPatch.html#matplotlib.patches.FancyArrowPatch)中的关键字 |\n",
    "\n",
    "如果设置了‘arrowstyle’关键字，以上关键字就不能使用。允许的值有：\n",
    "\n",
    "| 箭头的样式 | 属性                                          |\n",
    "| :--------- | :-------------------------------------------- |\n",
    "| `'-'`      | None                                          |\n",
    "| `'->'`     | head_length=0.4,head_width=0.2                |\n",
    "| `'-['`     | widthB=1.0,lengthB=0.2,angleB=None            |\n",
    "| `'|-|'`    | widthA=1.0,widthB=1.0                         |\n",
    "| `'-|>'`    | head_length=0.4,head_width=0.2                |\n",
    "| `'<-'`     | head_length=0.4,head_width=0.2                |\n",
    "| `'<->'`    | head_length=0.4,head_width=0.2                |\n",
    "| `'<|-'`    | head_length=0.4,head_width=0.2                |\n",
    "| `'<|-|>'`  | head_length=0.4,head_width=0.2                |\n",
    "| `'fancy'`  | head_length=0.4,head_width=0.4,tail_width=0.4 |\n",
    "| `'simple'` | head_length=0.5,head_width=0.5,tail_width=0.2 |\n",
    "| `'wedge'`  | tail_width=0.3,shrink_factor=0.5              |\n",
    "  \n",
    " 下图展现了不同的arrowstyle的不同形式\n",
    "<img src=\"https://matplotlib.org/_images/sphx_glr_fancyarrow_demo_001.png\" alt=\"image-20201108230524176\" style=\"zoom:50%;\" />\n",
    "\n",
    "[FancyArrowPatch]( https://matplotlib.org/api/_as_gen/matplotlib.patches.FancyArrowPatch.html#matplotlib.patches.FancyArrowPatch)的关键字包括:\n",
    "\n",
    "| Key             | Description                                                  |\n",
    "| :-------------- | :----------------------------------------------------------- |\n",
    "| arrowstyle      | 箭头的样式                                                   |\n",
    "| connectionstyle | 连接线的样式                                                 |\n",
    "| relpos          | 箭头起始点相对注释文本的位置，默认为 (0.5, 0.5)，即文本的中心，（0，0）表示左下角，（1，1）表示右上角 |\n",
    "| patchA          | 箭头起点处的图形（matplotlib.patches对象），默认是注释文字框 |\n",
    "| patchB          | 箭头终点处的图形（matplotlib.patches对象），默认为空         |\n",
    "| shrinkA         | 箭头起点的缩进点数，默认为2                                  |\n",
    "| shrinkB         | 箭头终点的缩进点数，默认为2                                  |\n",
    "| mutation_scale  | default is text size (in points)                             |\n",
    "| mutation_aspect | default is 1.                                                |\n",
    "| ?               | any key for [`matplotlib.patches.PathPatch`](https://matplotlib.org/api/_as_gen/matplotlib.patches.PathPatch.html#matplotlib.patches.PathPatch) |\n",
    "\n",
    "annotation_clip : 布尔值，可选参数，默认为空。设为True时，只有被注释点在axes时才绘制注释；设为False时，无论被注释点在哪里都绘制注释。仅当xycoords为‘data’时，默认值空相当于True。  \n",
    "\\*\\*kwargs:该参数接受任何[Text](https://matplotlib.org/api/text_api.html#matplotlib.text.Text)的参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#此代码主要给示范了不同的arrowstyle以及FancyArrowPatch的样式\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "fig, axs = plt.subplots(2, 2)\n",
    "x1, y1 = 0.3, 0.3\n",
    "x2, y2 = 0.7, 0.7\n",
    "\n",
    "ax = axs.flat[0]\n",
    "ax.plot([x1, x2], [y1, y2], \".\")\n",
    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
    "ax.add_artist(el)#在axes中创建一个artist\n",
    "ax.annotate(\"\",\n",
    "            xy=(x1, y1), xycoords='data',\n",
    "            xytext=(x2, y2), textcoords='data',\n",
    "            arrowprops=dict(arrowstyle=\"-\",#箭头的样式\n",
    "                            color=\"0.5\",\n",
    "                            patchB=None,\n",
    "                            shrinkB=0,\n",
    "                            connectionstyle=\"arc3,rad=0.3\",\n",
    "                            ),\n",
    "            )\n",
    "#在整个代码中使用Transform=ax.transAx表示坐标相对于axes的bounding box，其中(0，0)是轴的左下角，(1，1)是右上角。\n",
    "ax.text(.05, .95, \"connect\", transform=ax.transAxes, ha=\"left\", va=\"top\")\n",
    "\n",
    "ax = axs.flat[1]\n",
    "ax.plot([x1, x2], [y1, y2], \".\")\n",
    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
    "ax.add_artist(el)\n",
    "ax.annotate(\"\",\n",
    "            xy=(x1, y1), xycoords='data',\n",
    "            xytext=(x2, y2), textcoords='data',\n",
    "            arrowprops=dict(arrowstyle=\"-\",\n",
    "                            color=\"0.5\",\n",
    "                            patchB=el,#箭头终点处的图形\n",
    "                            shrinkB=0,\n",
    "                            connectionstyle=\"arc3,rad=0.3\",\n",
    "                            ),\n",
    "            )\n",
    "ax.text(.05, .95, \"clip\", transform=ax.transAxes, ha=\"left\", va=\"top\")\n",
    "\n",
    "ax = axs.flat[2]\n",
    "ax.plot([x1, x2], [y1, y2], \".\")\n",
    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
    "ax.add_artist(el)\n",
    "ax.annotate(\"\",\n",
    "            xy=(x1, y1), xycoords='data',\n",
    "            xytext=(x2, y2), textcoords='data',\n",
    "            arrowprops=dict(arrowstyle=\"-\",\n",
    "                            color=\"0.5\",\n",
    "                            patchB=el,\n",
    "                            shrinkB=5,\n",
    "                            connectionstyle=\"arc3,rad=0.3\",\n",
    "                            ),\n",
    "            )\n",
    "ax.text(.05, .95, \"shrink\", transform=ax.transAxes, ha=\"left\", va=\"top\")\n",
    "\n",
    "ax = axs.flat[3]\n",
    "ax.plot([x1, x2], [y1, y2], \".\")\n",
    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
    "ax.add_artist(el)\n",
    "ax.annotate(\"\",\n",
    "            xy=(x1, y1), xycoords='data',\n",
    "            xytext=(x2, y2), textcoords='data',\n",
    "            arrowprops=dict(arrowstyle=\"fancy\",\n",
    "                            color=\"0.5\",\n",
    "                            patchB=el,\n",
    "                            shrinkB=5,#箭头终点的缩进点数\n",
    "                            connectionstyle=\"arc3,rad=0.3\",\n",
    "                            ),\n",
    "            )\n",
    "ax.text(.05, .95, \"mutate\", transform=ax.transAxes, ha=\"left\", va=\"top\")\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(xlim=(0, 1), ylim=(0, 1), xticks=[], yticks=[], aspect=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "两个点之间的连接路径主要有connectionstyle和以下样式确定\n",
    "\n",
    "\n",
    "| Name     | Attrs                                         |\n",
    "| -------- | --------------------------------------------- |\n",
    "| `angle`  | angleA=90,angleB=0,rad=0.0                    |\n",
    "| `angle3` | angleA=90,angleB=0                            |\n",
    "| `arc`    | angleA=0,angleB=0,armA=None,armB=None,rad=0.0 |\n",
    "| `arc3`   | rad=0.0                                       |\n",
    "| `bar`    | armA=0.0,armB=0.0,fraction=0.3,angle=None     |  \n",
    "\n",
    "其中angle3 和 arc3 中的 3 意味着所得到的路径是二次样条段（ 三个控制点）   \n",
    "下面的例子丰富的展现了连接线的用法，可以参考学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x345.6 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def demo_con_style(ax, connectionstyle):\n",
    "    x1, y1 = 0.3, 0.2\n",
    "    x2, y2 = 0.8, 0.6\n",
    "\n",
    "    ax.plot([x1, x2], [y1, y2], \".\")\n",
    "    ax.annotate(\"\",\n",
    "                xy=(x1, y1), xycoords='data',\n",
    "                xytext=(x2, y2), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", color=\"0.5\",\n",
    "                                shrinkA=5, shrinkB=5,\n",
    "                                patchA=None, patchB=None,\n",
    "                                connectionstyle=connectionstyle,\n",
    "                                ),\n",
    "                )\n",
    "\n",
    "    ax.text(.05, .95, connectionstyle.replace(\",\", \",\\n\"),\n",
    "            transform=ax.transAxes, ha=\"left\", va=\"top\")\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(3, 5, figsize=(8, 4.8))\n",
    "demo_con_style(axs[0, 0], \"angle3,angleA=90,angleB=0\")\n",
    "demo_con_style(axs[1, 0], \"angle3,angleA=0,angleB=90\")\n",
    "demo_con_style(axs[0, 1], \"arc3,rad=0.\")\n",
    "demo_con_style(axs[1, 1], \"arc3,rad=0.3\")\n",
    "demo_con_style(axs[2, 1], \"arc3,rad=-0.3\")\n",
    "demo_con_style(axs[0, 2], \"angle,angleA=-90,angleB=180,rad=0\")\n",
    "demo_con_style(axs[1, 2], \"angle,angleA=-90,angleB=180,rad=5\")\n",
    "demo_con_style(axs[2, 2], \"angle,angleA=-90,angleB=10,rad=5\")\n",
    "demo_con_style(axs[0, 3], \"arc,angleA=-90,angleB=0,armA=30,armB=30,rad=0\")\n",
    "demo_con_style(axs[1, 3], \"arc,angleA=-90,angleB=0,armA=30,armB=30,rad=5\")\n",
    "demo_con_style(axs[2, 3], \"arc,angleA=-90,angleB=0,armA=0,armB=40,rad=0\")\n",
    "demo_con_style(axs[0, 4], \"bar,fraction=0.3\")\n",
    "demo_con_style(axs[1, 4], \"bar,fraction=-0.3\")\n",
    "demo_con_style(axs[2, 4], \"bar,angle=180,fraction=-0.2\")\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(xlim=(0, 1), ylim=(0, 1), xticks=[], yticks=[], aspect=1)\n",
    "fig.tight_layout(pad=0.2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#以下两个block懂了之后，annotate基本懂了\n",
    "#如果想更深入学习可以参看官网案例学习https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.annotate.html#matplotlib.axes.Axes.annotate\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "# 以步长0.005绘制一个曲线\n",
    "x = np.arange(0, 10, 0.005)\n",
    "y = np.exp(-x/2.) * np.sin(2*np.pi*x)\n",
    " \n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y)\n",
    "ax.set_xlim(0, 10)#设置x轴的范围\n",
    "ax.set_ylim(-1, 1)#设置x轴的范围\n",
    " \n",
    "# 被注释点的数据轴坐标和所在的像素\n",
    "xdata, ydata = 5, 0\n",
    "xdisplay, ydisplay = ax.transData.transform_point((xdata, ydata))\n",
    " \n",
    "# 设置注释文本的样式和箭头的样式\n",
    "bbox = dict(boxstyle=\"round\", fc=\"0.8\")\n",
    "arrowprops = dict(\n",
    "    arrowstyle = \"->\",\n",
    "    connectionstyle = \"angle,angleA=0,angleB=90,rad=10\")\n",
    " \n",
    "# 设置偏移量\n",
    "offset = 72\n",
    "# xycoords默认为'data'数据轴坐标，对坐标点（5,0）添加注释\n",
    "# 注释文本参考被注释点设置偏移量，向左2*72points，向上72points\n",
    "ax.annotate('data = (%.1f, %.1f)'%(xdata, ydata),\n",
    "            (xdata, ydata), xytext=(-2*offset, offset), textcoords='offset points',\n",
    "            bbox=bbox, arrowprops=arrowprops)\n",
    " \n",
    "# xycoords以绘图区左下角为参考，单位为像素\n",
    "# 注释文本参考被注释点设置偏移量，向右0.5*72points，向下72points\n",
    "disp = ax.annotate('display = (%.1f, %.1f)'%(xdisplay, ydisplay),\n",
    "            (xdisplay, ydisplay), xytext=(0.5*offset, -offset),\n",
    "            xycoords='figure pixels',\n",
    "            textcoords='offset points',\n",
    "            bbox=bbox, arrowprops=arrowprops)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "# 绘制一个极地坐标，再以0.001为步长，画一条螺旋曲线\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, polar=True)\n",
    "r = np.arange(0,1,0.001)\n",
    "theta = 2 * 2*np.pi * r\n",
    "line, = ax.plot(theta, r, color='#ee8d18', lw=3)\n",
    " \n",
    "# 对索引为800处画一个圆点，并做注释\n",
    "ind = 800\n",
    "thisr, thistheta = r[ind], theta[ind]\n",
    "ax.plot([thistheta], [thisr], 'o')\n",
    "ax.annotate('a polar annotation',\n",
    "            xy=(thistheta, thisr),  # 被注释点遵循极坐标系，坐标为角度和半径\n",
    "            xytext=(0.05, 0.05),    # 注释文本放在绘图区的0.05百分比处\n",
    "            textcoords='figure fraction',\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),# 箭头线为黑色，两端缩进5%\n",
    "            horizontalalignment='left',# 注释文本的左端和低端对齐到指定位置\n",
    "            verticalalignment='bottom',\n",
    "            )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### 7.字体的属性设置\n",
    " 字体设置一般有全局字体设置和自定义局部字体设置两种方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cmmi10\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "STIXSizeFourSym\n",
      "\n",
      "DejaVu Sans Display\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "STIXSizeFourSym\n",
      "\n",
      "STIXSizeThreeSym\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "cmr10\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "cmsy10\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "STIXSizeFiveSym\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "STIXSizeThreeSym\n",
      "\n",
      "DejaVu Serif Display\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "cmex10\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "STIXSizeOneSym\n",
      "\n",
      "STIXSizeTwoSym\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "cmb10\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "STIXSizeOneSym\n",
      "\n",
      "cmss10\n",
      "\n",
      "STIXSizeTwoSym\n",
      "\n",
      "cmtt10\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "Elephant\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Dubai\n",
      "\n",
      "Microsoft New Tai Lue\n",
      "\n",
      "Ravie\n",
      "\n",
      "Verdana\n",
      "\n",
      "Elephant\n",
      "\n",
      "Microsoft Tai Le\n",
      "\n",
      "Book Antiqua\n",
      "\n",
      "Gill Sans MT Ext Condensed Bold\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "FZShuTi\n",
      "\n",
      "Lucida Fax\n",
      "\n",
      "Eras Demi ITC\n",
      "\n",
      "STHupo\n",
      "\n",
      "Constantia\n",
      "\n",
      "Ebrima\n",
      "\n",
      "Symbol\n",
      "\n",
      "DengXian\n",
      "\n",
      "MS Reference Sans Serif\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Tahoma\n",
      "\n",
      "Arial\n",
      "\n",
      "Agency FB\n",
      "\n",
      "Corbel\n",
      "\n",
      "Javanese Text\n",
      "\n",
      "Castellar\n",
      "\n",
      "Lucida Sans\n",
      "\n",
      "FZYaoTi\n",
      "\n",
      "Lucida Sans\n",
      "\n",
      "Tahoma\n",
      "\n",
      "Lucida Sans Typewriter\n",
      "\n",
      "Gill Sans Ultra Bold Condensed\n",
      "\n",
      "STZhongsong\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Algerian\n",
      "\n",
      "Matura MT Script Capitals\n",
      "\n",
      "Franklin Gothic Demi\n",
      "\n",
      "Cooper Black\n",
      "\n",
      "Lucida Handwriting\n",
      "\n",
      "Mistral\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Lucida Bright\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Parchment\n",
      "\n",
      "Perpetua Titling MT\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Brush Script MT\n",
      "\n",
      "Freestyle Script\n",
      "\n",
      "Calibri\n",
      "\n",
      "Colonna MT\n",
      "\n",
      "Century Schoolbook\n",
      "\n",
      "Georgia\n",
      "\n",
      "Tw Cen MT\n",
      "\n",
      "Lucida Bright\n",
      "\n",
      "Gadugi\n",
      "\n",
      "Constantia\n",
      "\n",
      "STFangsong\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Gill Sans MT\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "Calisto MT\n",
      "\n",
      "Century Schoolbook\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Mongolian Baiti\n",
      "\n",
      "Lucida Sans Typewriter\n",
      "\n",
      "Berlin Sans FB\n",
      "\n",
      "Perpetua\n",
      "\n",
      "Lucida Fax\n",
      "\n",
      "Century Gothic\n",
      "\n",
      "Gill Sans MT\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Gadugi\n",
      "\n",
      "Rockwell\n",
      "\n",
      "Segoe Script\n",
      "\n",
      "FangSong\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Papyrus\n",
      "\n",
      "Californian FB\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "High Tower Text\n",
      "\n",
      "Leelawadee\n",
      "\n",
      "Rockwell Extra Bold\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Lucida Fax\n",
      "\n",
      "Microsoft Uighur\n",
      "\n",
      "Rage Italic\n",
      "\n",
      "Cambria\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Perpetua\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Tw Cen MT Condensed Extra Bold\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Wingdings 3\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Calisto MT\n",
      "\n",
      "Century Gothic\n",
      "\n",
      "Arial Rounded MT Bold\n",
      "\n",
      "Candara\n",
      "\n",
      "STSong\n",
      "\n",
      "Maiandra GD\n",
      "\n",
      "Microsoft Uighur\n",
      "\n",
      "Engravers MT\n",
      "\n",
      "Vladimir Script\n",
      "\n",
      "DengXian\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Calibri\n",
      "\n",
      "Gigi\n",
      "\n",
      "Book Antiqua\n",
      "\n",
      "Bernard MT Condensed\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Modern No. 20\n",
      "\n",
      "Britannic Bold\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Haettenschweiler\n",
      "\n",
      "Book Antiqua\n",
      "\n",
      "Californian FB\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Calisto MT\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Segoe Print\n",
      "\n",
      "Franklin Gothic Medium\n",
      "\n",
      "Bookman Old Style\n",
      "\n",
      "Lucida Sans Unicode\n",
      "\n",
      "Consolas\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "STKaiti\n",
      "\n",
      "Monotype Corsiva\n",
      "\n",
      "Microsoft PhagsPa\n",
      "\n",
      "Onyx\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Vivaldi\n",
      "\n",
      "Arial\n",
      "\n",
      "Franklin Gothic Medium\n",
      "\n",
      "Bookman Old Style\n",
      "\n",
      "Bradley Hand ITC\n",
      "\n",
      "Segoe MDL2 Assets\n",
      "\n",
      "Centaur\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Microsoft Sans Serif\n",
      "\n",
      "Script MT Bold\n",
      "\n",
      "Lucida Bright\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Myanmar Text\n",
      "\n",
      "Cambria\n",
      "\n",
      "Kristen ITC\n",
      "\n",
      "Sylfaen\n",
      "\n",
      "Leelawadee\n",
      "\n",
      "Rockwell Condensed\n",
      "\n",
      "Calibri\n",
      "\n",
      "High Tower Text\n",
      "\n",
      "Cambria\n",
      "\n",
      "Wingdings\n",
      "\n",
      "Courier New\n",
      "\n",
      "Lucida Calligraphy\n",
      "\n",
      "MT Extra\n",
      "\n",
      "Microsoft PhagsPa\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Calisto MT\n",
      "\n",
      "Consolas\n",
      "\n",
      "Lucida Sans\n",
      "\n",
      "Calibri\n",
      "\n",
      "Impact\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Tempus Sans ITC\n",
      "\n",
      "Verdana\n",
      "\n",
      "Gill Sans MT\n",
      "\n",
      "French Script MT\n",
      "\n",
      "Juice ITC\n",
      "\n",
      "Dubai\n",
      "\n",
      "Niagara Solid\n",
      "\n",
      "Lucida Fax\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Franklin Gothic Heavy\n",
      "\n",
      "Goudy Old Style\n",
      "\n",
      "Bell MT\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Arial\n",
      "\n",
      "Goudy Old Style\n",
      "\n",
      "Calibri\n",
      "\n",
      "Lucida Sans Typewriter\n",
      "\n",
      "Copperplate Gothic Bold\n",
      "\n",
      "Berlin Sans FB Demi\n",
      "\n",
      "Garamond\n",
      "\n",
      "Bell MT\n",
      "\n",
      "Bookman Old Style\n",
      "\n",
      "Gloucester MT Extra Condensed\n",
      "\n",
      "Segoe UI Symbol\n",
      "\n",
      "Arial\n",
      "\n",
      "Perpetua\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Harlow Solid Italic\n",
      "\n",
      "Baskerville Old Face\n",
      "\n",
      "Georgia\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Goudy Stout\n",
      "\n",
      "Franklin Gothic Book\n",
      "\n",
      "HoloLens MDL2 Assets\n",
      "\n",
      "STCaiyun\n",
      "\n",
      "Webdings\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Arial\n",
      "\n",
      "Poor Richard\n",
      "\n",
      "Playbill\n",
      "\n",
      "Franklin Gothic Medium Cond\n",
      "\n",
      "STLiti\n",
      "\n",
      "Bauhaus 93\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "Wide Latin\n",
      "\n",
      "Corbel\n",
      "\n",
      "Gill Sans MT\n",
      "\n",
      "Century\n",
      "\n",
      "Candara\n",
      "\n",
      "Corbel\n",
      "\n",
      "SimSun-ExtB\n",
      "\n",
      "Lucida Sans\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Corbel\n",
      "\n",
      "Chiller\n",
      "\n",
      "Dubai\n",
      "\n",
      "Candara\n",
      "\n",
      "Bell MT\n",
      "\n",
      "Magneto\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "LiSu\n",
      "\n",
      "Informal Roman\n",
      "\n",
      "Consolas\n",
      "\n",
      "Century Gothic\n",
      "\n",
      "Dubai\n",
      "\n",
      "Book Antiqua\n",
      "\n",
      "Segoe UI Emoji\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Courier New\n",
      "\n",
      "Harrington\n",
      "\n",
      "Ebrima\n",
      "\n",
      "Courier New\n",
      "\n",
      "Perpetua Titling MT\n",
      "\n",
      "Segoe UI Historic\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Agency FB\n",
      "\n",
      "Old English Text MT\n",
      "\n",
      "Lucida Bright\n",
      "\n",
      "Broadway\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Stencil\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "Segoe Print\n",
      "\n",
      "Verdana\n",
      "\n",
      "Ink Free\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "Eras Bold ITC\n",
      "\n",
      "Century Gothic\n",
      "\n",
      "Arial\n",
      "\n",
      "MS Outlook\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Lucida Sans Typewriter\n",
      "\n",
      "Curlz MT\n",
      "\n",
      "Century Schoolbook\n",
      "\n",
      "Tw Cen MT Condensed\n",
      "\n",
      "Marlett\n",
      "\n",
      "Georgia\n",
      "\n",
      "Georgia\n",
      "\n",
      "OCR A Extended\n",
      "\n",
      "Jokerman\n",
      "\n",
      "Blackadder ITC\n",
      "\n",
      "Lucida Console\n",
      "\n",
      "Tw Cen MT\n",
      "\n",
      "Tw Cen MT Condensed\n",
      "\n",
      "Bookman Old Style\n",
      "\n",
      "Showcard Gothic\n",
      "\n",
      "Kunstler Script\n",
      "\n",
      "MS Gothic\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Copperplate Gothic Light\n",
      "\n",
      "Perpetua\n",
      "\n",
      "Californian FB\n",
      "\n",
      "Candara\n",
      "\n",
      "MS Reference Specialty\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Bookshelf Symbol 7\n",
      "\n",
      "Franklin Gothic Demi Cond\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "Garamond\n",
      "\n",
      "Niagara Engraved\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Gill Sans MT Condensed\n",
      "\n",
      "DengXian\n",
      "\n",
      "SimSun\n",
      "\n",
      "Gabriola\n",
      "\n",
      "Eras Light ITC\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Candara\n",
      "\n",
      "KaiTi\n",
      "\n",
      "Constantia\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "YouYuan\n",
      "\n",
      "Snap ITC\n",
      "\n",
      "Viner Hand ITC\n",
      "\n",
      "MV Boli\n",
      "\n",
      "Consolas\n",
      "\n",
      "Palace Script MT\n",
      "\n",
      "Arial\n",
      "\n",
      "Century Schoolbook\n",
      "\n",
      "Berlin Sans FB\n",
      "\n",
      "Constantia\n",
      "\n",
      "Pristina\n",
      "\n",
      "Tw Cen MT\n",
      "\n",
      "Franklin Gothic Heavy\n",
      "\n",
      "Arial\n",
      "\n",
      "Microsoft Tai Le\n",
      "\n",
      "Franklin Gothic Demi\n",
      "\n",
      "Tw Cen MT\n",
      "\n",
      "Corbel\n",
      "\n",
      "Eras Medium ITC\n",
      "\n",
      "STXinwei\n",
      "\n",
      "Cambria\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "Arial\n",
      "\n",
      "Garamond\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "Footlight MT Light\n",
      "\n",
      "SimHei\n",
      "\n",
      "Calibri\n",
      "\n",
      "Forte\n",
      "\n",
      "Microsoft Yi Baiti\n",
      "\n",
      "Verdana\n",
      "\n",
      "Segoe Script\n",
      "\n",
      "Rockwell\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Microsoft New Tai Lue\n",
      "\n",
      "Candara\n",
      "\n",
      "Franklin Gothic Book\n",
      "\n",
      "Wingdings 2\n",
      "\n",
      "Bahnschrift\n",
      "\n",
      "Imprint MT Shadow\n",
      "\n",
      "Myanmar Text\n",
      "\n",
      "Rockwell\n",
      "\n",
      "STXihei\n",
      "\n",
      "Rockwell\n",
      "\n",
      "Edwardian Script ITC\n",
      "\n",
      "Courier New\n",
      "\n",
      "Gill Sans Ultra Bold\n",
      "\n",
      "Microsoft Himalaya\n",
      "\n",
      "Corbel\n",
      "\n",
      "Rockwell Condensed\n",
      "\n",
      "MingLiU-ExtB\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "STXingkai\n",
      "\n",
      "Felix Titling\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "Goudy Old Style\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#首先可以查看matplotlib所有可用的字体\n",
    "from matplotlib import font_manager\n",
    "font_family = font_manager.fontManager.ttflist\n",
    "font_name_list = [i.name for i in font_family]\n",
    "for font in font_name_list:\n",
    "    print(f'{font}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  [为方便在图中加入合适的字体，可以尝试了解中文字体的英文名称,该链接告诉了常用中文的英文名称](https://www.cnblogs.com/chendc/p/9298832.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#该block讲述如何在matplotlib里面，修改字体默认属性，完成全局字体的更改。\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']    # 指定默认字体为新宋体。\n",
    "plt.rcParams['axes.unicode_minus'] = False      # 解决保存图像时 负号'-' 显示为方块和报错的问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb0f5e1348>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#局部字体的修改方法1\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fontmg\n",
    "\n",
    "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "plt.plot(x, label='小示例图标签')\n",
    "\n",
    "# 直接用字体的名字。\n",
    "plt.xlabel('x 轴名称参数', fontproperties='Microsoft YaHei', fontsize=16)         # 设置x轴名称，采用微软雅黑字体\n",
    "plt.ylabel('y 轴名称参数', fontproperties='Microsoft YaHei', fontsize=14)         # 设置Y轴名称\n",
    "plt.title('坐标系的标题',  fontproperties='Microsoft YaHei', fontsize=20)\t\t  # 设置坐标系标题的字体\n",
    "plt.legend(loc='lower right', prop={\"family\": 'Microsoft YaHei'}, fontsize=10)    # 小示例图的字体设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb0f62bf08>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#局部字体的修改方法2\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fontmg\n",
    "\n",
    "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "plt.plot(x, label='小示例图标签')\n",
    "#fname为你系统中的字体库路径\n",
    "my_font1 = fontmg.FontProperties(fname=r'C:\\Windows\\Fonts\\simhei.ttf')      # 读取系统中的 黑体 字体。\n",
    "my_font2 = fontmg.FontProperties(fname=r'C:\\Windows\\Fonts\\simkai.ttf')      # 读取系统中的 楷体 字体。\n",
    "# fontproperties 设置中文显示，fontsize 设置字体大小\n",
    "plt.xlabel('x 轴名称参数', fontproperties=my_font1, fontsize=16)       # 设置x轴名称\n",
    "plt.ylabel('y 轴名称参数', fontproperties=my_font1, fontsize=14)       # 设置Y轴名称\n",
    "plt.title('坐标系的标题',  fontproperties=my_font2, fontsize=20)       # 标题的字体设置\n",
    "plt.legend(loc='lower right', prop=my_font1, fontsize=10)              # 小示例图的字体设置\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#这是以上学习内容的总结案例\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "fig.subplots_adjust(top=0.85)\n",
    "\n",
    "# Set titles for the figure and the subplot respectively\n",
    "fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n",
    "ax.set_title('axes title')\n",
    "\n",
    "ax.set_xlabel('xlabel')\n",
    "ax.set_ylabel('ylabel')\n",
    "\n",
    "# Set both x- and y-axis limits to [0, 10] instead of default [0, 1]\n",
    "ax.axis([0, 10, 0, 10])\n",
    "\n",
    "ax.text(3, 8, 'boxed italics text in data coords', style='italic',\n",
    "        bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})\n",
    "\n",
    "ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)\n",
    "font1 = {'family': 'Times New Roman',\n",
    "        'color':  'purple',\n",
    "        'weight': 'normal',\n",
    "        'size': 10,\n",
    "        }\n",
    "ax.text(3, 2, 'unicode: Institut für Festkörperphysik',fontdict=font1)\n",
    "ax.text(0.95, 0.01, 'colored text in axes coords',\n",
    "        verticalalignment='bottom', horizontalalignment='right',\n",
    "        transform=ax.transAxes,\n",
    "        color='green', fontsize=15)\n",
    "\n",
    "ax.plot([2], [1], 'o')\n",
    "ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.数学表达式\n",
    "在文本标签中使用数学表达式。有关MathText的概述，请参见 [写数学表达式](https://matplotlib.org/tutorials/text/mathtext.html#sphx-glr-tutorials-text-mathtext-py),但由于数学表达式的联系想必我们都在markdown语法和latex语法中多少有接触，故在此不继续展开，愿意深入学习的可以参看官方文档.下面是一个官方案例，供参考了解。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = np.sin(2*np.pi*t)\n",
    "\n",
    "plt.plot(t, s)\n",
    "plt.title(r'$\\alpha_i > \\beta_i$', fontsize=20)\n",
    "plt.text(1, -0.6, r'$\\sum_{i=0}^\\infty x_i$', fontsize=20)\n",
    "plt.text(0.6, 0.6, r'$\\mathcal{A}\\mathrm{sin}(2 \\omega t)$',\n",
    "         fontsize=20)\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('volts (mV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、Tick上的文本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设置tick（刻度）和ticklabel（刻度标签）也是可视化中经常需要操作的步骤，matplotlib既提供了自动生成刻度和刻度标签的模式（默认状态），同时也提供了许多让使用者灵活操控手动设置的方式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.简单模式\n",
    "可以使用axis的`set_ticks`方法手动设置标签位置，使用axis的`set_ticklabels`方法手动设置标签格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "x1 = np.linspace(0.0, 5.0, 100)\n",
    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用axis的set_ticks方法手动设置标签位置的例子\n",
    "fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)\n",
    "axs[0].plot(x1, y1)\n",
    "axs[1].plot(x1, y1)\n",
    "axs[1].xaxis.set_ticks(np.arange(0., 10.1, 2.))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用axis的set_ticklabels方法手动设置标签格式的例子\n",
    "fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)\n",
    "axs[0].plot(x1, y1)\n",
    "axs[1].plot(x1, y1)\n",
    "ticks = np.arange(0., 8.1, 2.)\n",
    "# list comprehension to get all tick labels...\n",
    "tickla = [f'{tick:1.2f}' for tick in ticks]\n",
    "axs[1].xaxis.set_ticks(ticks)\n",
    "axs[1].xaxis.set_ticklabels(tickla)\n",
    "#axs[1].set_xlim(axs[0].get_xlim())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Tick Locators and Formatters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除了上述的简单模式，还可以使用`Tick Locators and Formatters`完成对于刻度位置和刻度标签的设置。\n",
    "其中[Axis.set_major_locator](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_major_locator.html#matplotlib.axis.Axis.set_major_locator)和[Axis.set_minor_locator](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_minor_locator.html#matplotlib.axis.Axis.set_minor_locator)方法用来设置标签的位置，[Axis.set_major_formatter](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_major_formatter.html#matplotlib.axis.Axis.set_major_formatter)和[Axis.set_minor_formatter](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_minor_formatter.html#matplotlib.axis.Axis.set_minor_formatter)方法用来设置标签的格式。这种方式的好处是不用显式地列举出刻度值列表。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "set_major_formatter和set_minor_formatter这两个formatter格式命令可以接收字符串格式（matplotlib.ticker.StrMethodFormatter）或函数参数（matplotlib.ticker.FuncFormatter）来设置刻度值的格式 。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 接收字符串格式的例子\n",
    "fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True)\n",
    "for n, ax in enumerate(axs.flat):\n",
    "    ax.plot(x1*10., y1)\n",
    "\n",
    "formatter = matplotlib.ticker.FormatStrFormatter('%1.1f')\n",
    "axs[0, 1].xaxis.set_major_formatter(formatter)\n",
    "\n",
    "formatter = matplotlib.ticker.FormatStrFormatter('-%1.1f')\n",
    "axs[1, 0].xaxis.set_major_formatter(formatter)\n",
    "\n",
    "formatter = matplotlib.ticker.FormatStrFormatter('%1.5f')\n",
    "axs[1, 1].xaxis.set_major_formatter(formatter)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 接收函数的例子\n",
    "def formatoddticks(x, pos):\n",
    "    \"\"\"Format odd tick positions.\"\"\"\n",
    "    if x % 2:\n",
    "        return f'{x:1.2f}'\n",
    "    else:\n",
    "        return ''\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)\n",
    "ax.plot(x1, y1)\n",
    "ax.xaxis.set_major_formatter(formatoddticks)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "至于刻度的位置，matplotlib给出了常用的几种locator的类型。如果要绘制更复杂的图，可以先设置locator的类型，然后通过set_major_locator(locator)绘制即可\n",
    "- locator=plt.MaxNLocator(nbins=7)  \n",
    "- locator=plt.FixedLocator(locs=[0,0.5,1.5,2.5,3.5,4.5,5.5,6])#直接指定刻度所在的位置  \n",
    "- locator=plt.AutoLocator()#自动分配刻度值的位置  \n",
    "- locator=plt.IndexLocator(offset=0.5, base=1)#面元间距是1，从0.5开始  \n",
    "- locator=plt.MultipleLocator(1.5)#将刻度的标签设置为1.5的倍数  \n",
    "- locator=plt.LinearLocator(numticks=5)#线性划分5等分，4个刻度  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 接收各种locator的例子\n",
    "fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True)\n",
    "for n, ax in enumerate(axs.flat):\n",
    "    ax.plot(x1*10., y1)\n",
    "\n",
    "locator = matplotlib.ticker.AutoLocator()\n",
    "axs[0, 0].xaxis.set_major_locator(locator)\n",
    "\n",
    "locator = matplotlib.ticker.MaxNLocator(nbins=10)\n",
    "axs[0, 1].xaxis.set_major_locator(locator)\n",
    "\n",
    "\n",
    "locator = matplotlib.ticker.MultipleLocator(5)\n",
    "axs[1, 0].xaxis.set_major_locator(locator)\n",
    "\n",
    "\n",
    "locator = matplotlib.ticker.FixedLocator([0,7,14,21,28])\n",
    "axs[1, 1].xaxis.set_major_locator(locator)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 此外`matplotlib.dates` 模块还提供了特殊的设置日期型刻度格式和位置的方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "import datetime\n",
    "# 特殊的日期型locator和formatter\n",
    "locator = mdates.DayLocator(bymonthday=[1,15,25])\n",
    "formatter = mdates.DateFormatter('%b %d')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "base = datetime.datetime(2017, 1, 1, 0, 0, 1)\n",
    "time = [base + datetime.timedelta(days=x) for x in range(len(x1))]\n",
    "ax.plot(time, y1)\n",
    "ax.tick_params(axis='x', rotation=70)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**一些相关案例**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<a list of 14 Line2D ticklines objects>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#一般绘图时会自动创建刻度，而如果通过set_ticks创建刻度可能会导致tick的范围与所绘制图形的范围不一致的问题。\n",
    "#所以在下面的案例中，axs[1]中set_xtick的设置要与数据范围所对应，然后再通过set_xticklabels设置刻度所对应的标签\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(2, 1, figsize=(6, 4), tight_layout=True)\n",
    "x1 = np.linspace(0.0, 6.0, 100)\n",
    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n",
    "axs[0].plot(x1, y1)\n",
    "axs[0].set_xticks([0,1,2,3,4,5,6])\n",
    "\n",
    "axs[1].plot(x1, y1)\n",
    "axs[1].set_xticks([0,1,2,3,4,5,6])#要将x轴的刻度放在数据范围中的哪些位置\n",
    "axs[1].set_xticklabels(['zero','one', 'two', 'three', 'four', 'five','six'],#设置刻度对应的标签\n",
    "                   rotation=30, fontsize='small')#rotation选项设定x刻度标签倾斜30度。\n",
    "axs[1].xaxis.set_ticks_position('bottom')#set_ticks_position()方法是用来设置刻度所在的位置，常用的参数有bottom、top、both、none\n",
    "print(axs[1].xaxis.get_ticklines())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "在普通的绘图中，我们可以直接通过上图的set_ticks进行设置刻度的位置，缺点是需要自己指定或者接受matplotlib默认给定的刻度。当需要更改刻度的位置时，matplotlib给了常用的几种locator的类型。如果要绘制更复杂的图，可以先设置locator的类型，然后通过axs.xaxis.set_major_locator(locator)绘制即可  \n",
    "locator=plt.MaxNLocator(nbins=7)  \n",
    "locator=plt.FixedLocator(locs=[0,0.5,1.5,2.5,3.5,4.5,5.5,6])#直接指定刻度所在的位置  \n",
    "locator=plt.AutoLocator()#自动分配刻度值的位置  \n",
    "locator=plt.IndexLocator(offset=0.5, base=1)#面元间距是1，从0.5开始  \n",
    "locator=plt.MultipleLocator(1.5)#将刻度的标签设置为1.5的倍数  \n",
    "locator=plt.LinearLocator(numticks=5)#线性划分5等分，4个刻度  \n",
    "\n",
    "axs.xaxis.set_major_locator(locator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#这个案例中展示了如何进行坐标轴的移动，如何更改刻度值的样式\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.linspace(-3,3,50)\n",
    "y1 = 2*x+1\n",
    "y2 = x**2\n",
    "plt.figure()\n",
    "plt.plot(x,y2)\n",
    "plt.plot(x,y1,color='red',linewidth=1.0,linestyle = '--')\n",
    "plt.xlim((-3,5))\n",
    "plt.ylim((-3,5))\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "new_ticks1 = np.linspace(-3,5,5)\n",
    "plt.xticks(new_ticks1)\n",
    "plt.yticks([-2,0,2,5],[r'$one\\ shu$',r'$\\alpha$',r'$three$',r'four'])\n",
    "'''\n",
    "上一行代码是将y轴上的小标改成文字,其中，空格需要增加\\，即'\\ ',$可将格式更改成数字模式，如果需要输入数学形式的α，则需要用\\转换，即\\alpha\n",
    "如果使用面向对象的命令进行画图，那么下面两行代码可以实现与 plt.yticks([-2,0,2,5],[r'$one\\ shu$',r'$\\alpha$',r'$three$',r'four']) 同样的功能\n",
    "axs.set_yticks([-2,0,2,5])\n",
    "axs.set_yticklabels([r'$one\\ shu$',r'$\\alpha$',r'$three$',r'four'])\n",
    "'''\n",
    "ax = plt.gca()#gca = 'get current axes' 获取现在的轴\n",
    "'''\n",
    "ax = plt.gca()是获取当前的axes，其中gca代表的是get current axes。\n",
    "fig=plt.gcf是获取当前的figure，其中gcf代表的是get current figure。\n",
    "\n",
    "许多函数都是对当前的Figure或Axes对象进行处理，\n",
    "例如plt.plot()实际上会通过plt.gca()获得当前的Axes对象ax，然后再调用ax.plot()方法实现真正的绘图。\n",
    "\n",
    "而在本例中则可以通过ax.spines方法获得当前顶部和右边的轴并将其颜色设置为不可见\n",
    "然后将左边轴和底部的轴所在的位置重新设置\n",
    "最后再通过set_ticks_position方法设置ticks在x轴或y轴的位置，本示例中因所设置的bottom和left是ticks在x轴或y轴的默认值，所以这两行的代码也可以不写\n",
    "'''\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['left'].set_position(('data',0))\n",
    "ax.spines['bottom'].set_position(('data',0))#axes 百分比\n",
    "ax.xaxis.set_ticks_position('bottom')   #设置ticks在x轴的位置\n",
    "ax.yaxis.set_ticks_position('left')     #设置ticks在y轴的位置\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、[legend](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend)（图例）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图例的设置会使用一些常见术语，为了清楚起见，这些术语在此处进行说明：\n",
    "##### legend entry（图例条目）\n",
    "图例有一个或多个legend entries组成。一个entry由一个key和一个label组成。\n",
    "##### legend key（图例键）\n",
    "每个 legend label左面的colored/patterned marker（彩色/图案标记）\n",
    "##### legend label（图例标签）\n",
    "描述由key来表示的handle的文本\n",
    "##### legend handle（图例句柄）\n",
    "用于在图例中生成适当图例条目的原始对象\n",
    "\n",
    "以下面这个图为例，右侧的方框中的共有两个legend entry；两个legend key，分别是一个蓝色和一个黄色的legend key；两个legend label，一个名为‘Line up’和一个名为‘Line Down’的legend label"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib.pyplot.legend(\\*args, \\*\\*kwargs)\n",
    "\n",
    "**参数**：此方法接受以下描述的参数:   \n",
    "\n",
    "| keyword         | Description                                                  |\n",
    "| --------------- | ------------------------------------------------------------ |\n",
    "| loc             | Location code string, or tuple (see below).图例所有figure位置 |\n",
    "| prop            | the font property字体参数                                    |\n",
    "| fontsize        | the font size (used only if prop is not specified)           |\n",
    "| markerscale     | the relative size of legend markers vs. original图例标记与原始标记的相对大小 |\n",
    "| markerfirst     | If True (default), marker is to left of the label.如果为True，则图例标记位于图例标签的左侧 |\n",
    "| numpoints       | the number of points in the legend for line为线条图图例条目创建的标记点数 |\n",
    "| scatterpoints   | the number of points in the legend for scatter plot为散点图图例条目创建的标记点数 |\n",
    "| scatteryoffsets | a list of yoffsets for scatter symbols in legend为散点图图例条目创建的标记的垂直偏移量 |\n",
    "| frameon         | If True, draw the legend on a patch (frame).控制是否应在图例周围绘制框架 |\n",
    "| fancybox        | If True, draw the frame with a round fancybox.控制是否应在构成图例背景的FancyBboxPatch周围启用圆边 |\n",
    "| shadow          | If True, draw a shadow behind legend.控制是否在图例后面画一个阴 |\n",
    "| framealpha      | Transparency of the frame.控制图例框架的 Alpha 透明度        |\n",
    "| edgecolor       | Frame edgecolor.                                             |\n",
    "| facecolor       | Frame facecolor.                                             |\n",
    "| ncol            | number of columns 设置图例分为n列展示                        |\n",
    "| borderpad       | the fractional whitespace inside the legend border图例边框的内边距 |\n",
    "| labelspacing    | the vertical space between the legend entries图例条目之间的垂直间距 |\n",
    "| handlelength    | the length of the legend handles 图例句柄的长度              |\n",
    "| handleheight    | the height of the legend handles 图例句柄的高度              |\n",
    "| handletextpad   | the pad between the legend handle and text 图例句柄和文本之间的间距 |\n",
    "| borderaxespad   | the pad between the axes and legend border轴与图例边框之间的距离 |\n",
    "| columnspacing   | the spacing between columns 列间距                           |\n",
    "| title           | the legend title                                             |\n",
    "| bbox_to_anchor  | the bbox that the legend will be anchored.指定图例在轴的位置 |\n",
    "| bbox_transform  | the transform for the bbox. transAxes if None.               |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "常用的几个参数：\n",
    "\n",
    "(1)设置图列位置\n",
    "\n",
    "plt.legend(loc='upper center') 等同于plt.legend(loc=9)\n",
    "\n",
    "\n",
    "\n",
    "| |  |  |\n",
    "| ------------------------------------------------------- | ------------------------------------------ | ------------------------------------------------------------ |\n",
    "|  0: ‘best'  \n",
    "1: ‘upper right'  \n",
    "2: ‘upper left'  \n",
    "3: ‘lower left'                                                        |   \n",
    "4: ‘lower right'  \n",
    "5: ‘right'  \n",
    "6: ‘center left'                                           |    \n",
    "7: ‘center right'  \n",
    "8: ‘lower center'  \n",
    "9: ‘upper center'  \n",
    "10: ‘center'                                                           |  \n",
    "\n",
    "\n",
    "(2)设置图例字体大小\n",
    "\n",
    "fontsize : int or float or {‘xx-small’, ‘x-small’, ‘small’, ‘medium’, ‘large’, ‘x-large’, ‘xx-large’}\n",
    "\n",
    "(3)设置图例边框及背景\n",
    "\n",
    "plt.legend(loc='best',frameon=False) #去掉图例边框  \n",
    "plt.legend(loc='best',edgecolor='blue') #设置图例边框颜色  \n",
    "plt.legend(loc='best',facecolor='blue') #设置图例背景颜色,若无边框,参数无效\n",
    "\n",
    "(4)设置图例标题\n",
    "\n",
    "legend = plt.legend([\"CH\", \"US\"], title='China VS Us')\n",
    "\n",
    "(5)设置图例名字及对应关系\n",
    "\n",
    "legend = plt.legend([p1, p2], [\"CH\", \"US\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb10938b08>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line_up, = plt.plot([1, 2, 3], label='Line 2')\n",
    "line_down, = plt.plot([3, 2, 1], label='Line 1')\n",
    "plt.legend([line_up, line_down], ['Line Up', 'Line Down'],loc=5, title='line',frameon=False)#loc参数设置图例所在的位置，title设置图例的标题，frameon参数将图例边框给去掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb0f042148>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#这个案例是显示多图例legend\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.random.uniform(-1, 1, 4)\n",
    "y = np.random.uniform(-1, 1, 4)\n",
    "p1, = plt.plot([1,2,3])\n",
    "p2, = plt.plot([3,2,1])\n",
    "l1 = plt.legend([p2, p1], [\"line 2\", \"line 1\"], loc='upper left')\n",
    " \n",
    "p3 = plt.scatter(x[0:2], y[0:2], marker = 'D', color='r')\n",
    "p4 = plt.scatter(x[2:], y[2:], marker = 'D', color='g')\n",
    "# 下面这行代码由于添加了新的legend，所以会将l1从legend中给移除\n",
    "plt.legend([p3, p4], ['label', 'label1'], loc='lower right', scatterpoints=1)\n",
    "# 为了保留之前的l1这个legend，所以必须要通过plt.gca()获得当前的axes，然后将l1作为单独的artist\n",
    "plt.gca().add_artist(l1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考资料\n",
    "[1.Python学习笔记（4）——Matplotlib中的annotate（注解）的用法](https://blog.csdn.net/leaf_zizi/article/details/82886755)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业\n",
    "1.尝试在一张中运用所讲过的功能，对title、text、xlable、ylabel、数学表达式、tick and ticklabel、legend进行详细的设计.  \n",
    "2.阅读你可能用到文献或者相关书籍，思考自己如何才能通过学过的例子将自己认为比较好看的图给复现出来."
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